Evaluating Preprocessing Techniques for Unsupervised Mode Detection in Irish Traditional Music

Significant computational research has been dedicated to automatic key and mode detection in Western tonal music, particularly within the major and minor modes. However, limited research has focused on identifying alternative diatonic modes in traditional and folk music contexts. This paper addresses this gap by comparing the effectiveness of various preprocessing techniques in unsupervised machine learning for diatonic mode detection. Using a dataset of Irish folk music that incorporates diatonic modes such as Ionian, Dorian, Mixolydian, and Aeolian, we assess how different preprocessing approaches influence clustering accuracy and mode distinction. By examining multiple feature transformations and reductions, this study highlights the impact of preprocessing choices on clustering performance, aiming to optimize the unsupervised classification of diatonic modes in folk music traditions.